GAMESlab

The System Optimum, an optimal traffic assignment that minimizes the total travel costs on the road network is usually only referred to as a comparison to self-emerging user equilibrium. In this paper we investigate how different behavioral aspects of drivers can self-organize towards a system optimum that minimizes travel costs while providing benefits and preserving equity among drivers. We present a simple binary route-choice Agent-Based Model that provides a disaggregated view of driver behavior and a unique understanding of the potential of cognitive reinforcement models to effect a convergence to user equilibrium and a shift in driver behavior toward a system optimum without the need for an enforcing traffic policy such as tolls.